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Divestment may burst the carbon bubble if investors' beliefs tip to anticipating strong future climate policy

Author

Listed:
  • Birte Ewers
  • Jonathan F. Donges
  • Jobst Heitzig
  • Sonja Peterson
Abstract
To achieve the ambitious aims of the Paris climate agreement, the majority of fossil-fuel reserves needs to remain underground. As current national government commitments to mitigate greenhouse gas emissions are insufficient by far, actors such as institutional and private investors and the social movement on divestment from fossil fuels could play an important role in putting pressure on national governments on the road to decarbonization. Using a stochastic agent-based model of co-evolving financial market and investors' beliefs about future climate policy on an adaptive social network, here we find that the dynamics of divestment from fossil fuels shows potential for social tipping away from a fossil-fuel based economy. Our results further suggest that socially responsible investors have leverage: a small share of 10--20\,\% of such moral investors is sufficient to initiate the burst of the carbon bubble, consistent with the Pareto Principle. These findings demonstrate that divestment has potential for contributing to decarbonization alongside other social movements and policy instruments, particularly given the credible imminence of strong international climate policy. Our analysis also indicates the possible existence of a carbon bubble with potentially destabilizing effects to the economy.

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  • Birte Ewers & Jonathan F. Donges & Jobst Heitzig & Sonja Peterson, 2019. "Divestment may burst the carbon bubble if investors' beliefs tip to anticipating strong future climate policy," Papers 1902.07481, arXiv.org.
    • Handle: RePEc:arx:papers:1902.07481
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    References listed on IDEAS

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    1. Sandrine Jacob Leal & Mauro Napoletano & Andrea Roventini & Giorgio Fagiolo, 2016. "Rock around the clock: An agent-based model of low- and high-frequency trading," Journal of Evolutionary Economics, Springer, vol. 26(1), pages 49-76, March.
    2. Juan Carlos Castilla-Rho & Rodrigo Rojas & Martin S. Andersen & Cameron Holley & Gregoire Mariethoz, 2017. "Social tipping points in global groundwater management," Nature Human Behaviour, Nature, vol. 1(9), pages 640-649, September.
    3. Justin Ritchie & Hadi Dowlatabadi, 2015. "Divest from the Carbon Bubble? Reviewing the Implications and Limitations of Fossil Fuel Divestment for Institutional Investors," Review of Economics & Finance, Better Advances Press, Canada, vol. 5, pages 59-80, May.
    4. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    5. J. Farmer & Cameron Hepburn & Penny Mealy & Alexander Teytelboym, 2015. "A Third Wave in the Economics of Climate Change," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(2), pages 329-357, October.
    6. Christophe McGlade & Paul Ekins, 2015. "The geographical distribution of fossil fuels unused when limiting global warming to 2 °C," Nature, Nature, vol. 517(7533), pages 187-190, January.
    7. Richard J. Millar & Cameron Hepburn & John Beddington & Myles R. Allen, 2018. "Principles to guide investment towards a stable climate," Nature Climate Change, Nature, vol. 8(1), pages 2-4, January.
    8. Hans Joachim Schellnhuber & Stefan Rahmstorf & Ricarda Winkelmann, 2016. "Why the right climate target was agreed in Paris," Nature Climate Change, Nature, vol. 6(7), pages 649-653, July.
    9. Stefano Battiston & Antoine Mandel & Irene Monasterolo & Franziska Schütze & Gabriele Visentin, 2017. "A climate stress-test of the financial system," Nature Climate Change, Nature, vol. 7(4), pages 283-288, April.
    10. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    11. J. Doyne Farmer & Duncan Foley, 2009. "The economy needs agent-based modelling," Nature, Nature, vol. 460(7256), pages 685-686, August.
    12. Hong, Harrison & Kacperczyk, Marcin, 2009. "The price of sin: The effects of social norms on markets," Journal of Financial Economics, Elsevier, vol. 93(1), pages 15-36, July.
    13. B. LeBaron, 2001. "A builder's guide to agent-based financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 254-261.
    14. Nico Bauer & Christophe McGlade & Jérôme Hilaire & Paul Ekins, 2018. "Divestment prevails over the green paradox when anticipating strong future climate policies," Nature Climate Change, Nature, vol. 8(2), pages 130-134, February.
    15. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    16. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2007. "Agent-based Models of Financial Markets," Papers physics/0701140, arXiv.org.
    17. Howard Covington & James Thornton & Cameron Hepburn, 2016. "Global warming: Shareholders must vote for climate-change mitigation," Nature, Nature, vol. 530(7589), pages 156-156, February.
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